arXiv subjects

Chris Gaiteri

Publications and source records attributed to Chris Gaiteri.

3 recordsLinked to original sources

arXiv2025

An experimental study of the influence of anonymous information on social media users

Increasingly, people use social media for their day-to-day interactions and as a source of information, even though much of this information is practically anonymous. This raises the question: does anonymous information influence its recipients? We conducted an online, two-phase, preregistered experiment using a nationally representative sample of participants from the U.S. to find the answer. To avoid biases of opinions among participants, in the first phase, each participant examines ten Rorschach inkblots and chooses one of four opinions assigned to each inkblot. In the second phase, the participants are randomly assigned to one of four distinct information conditions and are asked to revisit their opinions for the same ten inkblots. Conditions ranged from repeating phase one to receiving anonymous comments about certain opinions. Results were consistent with the preregistration. Importantly, anonymous comments shown in phase two influence up to half of the participants' opinion selections. To better understand the role of anonymous comments in influencing the selections of opinions, we implemented agent-based modeling (ABM). ABM results suggest that a straightforward mechanism can explain the impact of such information. Overall, our results indicate that even anonymous information can have a significant impact on its recipients, potentially altering their popularity rankings. However, the strength of such influence weakens when recipients' confidence in their selections increases. Additionally, we found that participants' confidence in the first phase is inversely related to the number of change opinions.

cs.SI↗

arXiv2016

Supporting novel biomedical research via multilayer collaboration networks

The value of research containing novel combinations of molecules can be seen in many innovative and award-winning research programs. Despite calls to use innovative approaches to address common diseases, an increasing majority of research funding goes toward "safe" incremental research. Counteracting this trend by nurturing novel and potentially transformative scientific research is challenging, it must be supported in competition with established research programs. Therefore, we propose a tool that helps to resolve the tension between safe but fundable research vs. high-risk but potentially transformational research. It does this by identifying hidden overlapping interest around novel molecular research topics. Specifically, it identifies paths of molecular interactions that connect research topics and hypotheses that would not typically be associated, as the basis for scientific collaboration. Because these collaborations are related to the scientists' present trajectory, they are low risk and can be initiated rapidly. Unlike most incremental steps, these collaborations have the potential for leaps in understanding, as they reposition research for novel disease applications. We demonstrate the use of this tool to identify scientists who could contribute to understanding the cellular role of genes with novel associations with Alzheimer's disease, which have not been thoroughly characterized, in part due to the funding emphasis on established research.

cs.SI↗

arXiv2015

Identifying robust communities and multi-community nodes by combining top-down and bottom-up approaches to clustering

Biological functions are carried out by groups of interacting molecules, cells or tissues, known as communities. Membership in these communities may overlap when biological components are involved in multiple functions. However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult to replicate, because traditional methods are sensitive to noise and parameter settings. These aspects of traditional clustering methods limit our ability to detect biological communities, and therefore our ability to understand biological functions. To address these limitations and detect robust overlapping biological communities, we propose an unorthodox clustering method called SpeakEasy which identifies communities using top-down and bottom-up approaches simultaneously. Specifically, nodes join communities based on their local connections, as well as global information about the network structure. This method can quantify the stability of each community, automatically identify the number of communities, and quickly cluster networks with hundreds of thousands of nodes. SpeakEasy shows top performance on synthetic clustering benchmarks and accurately identifies meaningful biological communities in a range of datasets, including: gene microarrays, protein interactions, sorted cell populations, electrophysiology and fMRI brain imaging.

cs.CE↗